In recent developments, Microsoft Azure’s new AI toolkit has introduced a significant breakthrough in the field of artificial intelligence by making it easier to customize OpenAI large language models (LLMs) for a wide range of applications. These models are instrumental in developing sophisticated AI-powered solutions such as advanced chatbots and agent-powered workflows. However, leveraging these off-the-shelf models to their full potential often requires prompt engineering, which involves crafting prompts to structure AI responses effectively. Despite the advantages of prompt engineering, limitations exist, particularly concerning the context window size and associated costs. The integration of Azure AI Foundry addresses these limitations by providing a framework to fine-tune LLMs using techniques like Low-rank Adaptation (LoRA), ultimately delivering higher quality results with fewer tokens and reduced risk of issues.
1. Gather and Prepare Training and Validation Data
To begin the fine-tuning process, collecting and preparing your training and validation data correctly is crucial. The data must be formatted according to the requirements of the specific LLM you’re working with. For instance, if you are using GPT-3.5 or GPT-4 models, the data should be in JSON Lines (JSONL) format. JSONL is a variant of JSON where each record is on a separate line, making it ideal for building sample documents containing conversational data. Each line in the file typically includes user content, a prompt, and the expected AI response. This structured approach facilitates multi-turn chat sessions as training data, allowing models to learn and adapt more effectively.
In addition to text-based content, training data can include URLs linking to images and multimedia, thus enabling the fine-tuning of multimodal AI models. If you are unfamiliar with the JSONL format, several tools can aid in the conversion process. One such tool is the OpenAI CLI, which installs using Python’s pip tool. You can then use commands like openai tools fine_tunes.prepare_data -f with local files to convert CSV, Excel, or JSON data into the necessary JSONL format.
2. Initiate Custom Model Creation
Once your training data is ready, the next step is to initiate the creation of a custom model in Azure AI Foundry. The platform provides a user-friendly Create custom model wizard that guides you through the fine-tuning process. This involves uploading your training data, setting task parameters, and defining the scope of the fine-tuning project. The Azure AI Foundry supports various models from different providers, including well-known models like Meta’s Llama and various models from Hugging Face. You will need an Azure account with the Cognitive Services OpenAI Contributor role to gain access to these tools and upload the required training data.
When working with the Azure OpenAI tools, it’s essential to ensure you are operating within a region that supports fine-tuning. As of now, regions like North Central US and Sweden Central have the necessary infrastructure to facilitate this process. Additionally, knowing the maximum number of tokens available for fine-tuning is essential, as this can vary between models and impact the overall training process.
3. Format Training Data
Ensuring your training data is in the correct format cannot be overstated. For GPT models, the JSONL format is imperative, and creating this from your raw data can either be a simple or complex task depending on your resources. The OpenAI CLI tool significantly aids this process by converting data from formats like CSV or Excel into JSONL. The ability to script the formatting of training data using Python and OpenAI CLI offers considerable flexibility and ensures the data adheres to the required structure.
Training data is pivotal in shaping the AI model’s ability to generate coherent and contextually appropriate responses. A well-prepared set of training data can make a significant difference in the model’s performance, especially in handling complex queries and generating nuanced responses. Therefore, spending ample time and resources on correctly formatting and structuring your training data is advisable.
4. Upload Training Data
The subsequent step involves uploading your meticulously prepared training data to Azure AI Foundry. This data can either be uploaded directly from local files or, more efficiently, from Azure Storage if you have used services like Microsoft Fabric to construct your training sets. By uploading the training data to Azure Blob Storage in advance, you can streamline the process and minimize the risk of interruptions or errors during the upload process.
Azure AI Foundry provides a centralized platform to manage and access your training data, ensuring that all necessary resources are available in one place. This integrated approach enhances the workflow and simplifies the task of fine-tuning models for specific applications.
5. Optional: Upload Validation Data
Uploading validation data, while optional, can be extremely beneficial for the fine-tuning process. Validation data should be formatted similarly to your training data, ensuring consistency and reliability in the model’s performance evaluation. Testing the model against a separate set of validation data allows you to assess its accuracy and effectiveness before deployment comprehensively.
Validation data plays a crucial role in identifying potential issues and fine-tuning the model to ensure optimal performance. While it is not mandatory, incorporating this step can significantly enhance the quality of the final AI model, making it more robust and reliable in real-world applications.
6. Set Tuning Parameters
Defining the appropriate tuning parameters is a critical step in the fine-tuning process. Parameters such as batch sizes, learning rates, and the number of cycles through the training data can drastically impact the outcome of the tuning process. Azure AI Foundry offers a range of customizable settings that you can either set manually based on your requirements or leave on default values, which are determined through an analysis of your training data.
These parameters will guide the fine-tuning process, influencing how the model adjusts and learns from the training data. Incorrect parameter settings could either overfit or underfit the model, leading to suboptimal performance. Therefore, understanding and selecting the right parameters is crucial for achieving the best possible results.
7. Start the Fine-Tuning Process
With everything in place, you can start the fine-tuning process via Azure AI Foundry. This batch process requires substantial computational resources and may take several hours to complete, depending on the complexity and size of your model and training data. Azure AI Foundry’s tools provide real-time updates, allowing you to monitor the status and results of your fine-tuning job. Each pass through the training data generates checkpoints, which are intermediate versions of the model that you can evaluate with your code to measure progress before the job completes.
The iterative nature of the fine-tuning process means that multiple checkpoints are generated, with the possibility of adjusting parameters based on initial results. This iterative evaluation and adjustment are crucial for refining the model and ensuring that the final version meets your specific requirements.
8. Evaluate Checkpoints
Evaluating the checkpoints produced during the training process is essential for selecting the best version of your fine-tuned model. These checkpoints provide glimpses into the model’s ongoing development and allow you to test its performance with your code. By comparing different checkpoints, you can identify the most effective version before the entire fine-tuning job is completed.
Having access to multiple checkpoints ensures that you can continuously assess and refine the model, making necessary adjustments to improve its performance. This step is vital for achieving high-quality results and ensuring the AI model meets your application’s specific needs.
9. Ensure Model Safety
Model safety is paramount in deploying AI solutions, and Microsoft’s stringent AI safety rules apply to all fine-tuned models. Before making a model public, it must undergo comprehensive testing and evaluation in private workspaces to ensure its behavior aligns with ethical and safety standards. Microsoft’s AI safety tools scrutinize the fine-tuned model for harmful content and inappropriate behavior, ensuring that it is safe for deployment.
The AI safety rules also ensure that your training data remains private and secure, reducing the risk of confidential data leaks. The thorough safety evaluation process guarantees that only safe and reliable models are deployed, mitigating potential risks associated with AI operations.
10. Deploy the Fine-Tuned Model
Once the fine-tuned model has been thoroughly tested and deemed safe, you can deploy it as a standard AI endpoint using Azure AI APIs and SDKs. This deployment allows you to integrate the model into your applications, leveraging its customized capabilities to enhance user experiences and operational efficiency. It’s important to note that deployed models can only run in regions that support fine-tuning, offering flexibility in model management and application deployment.
Furthermore, deployed models have a limited usage window and must be redeployed if unused for 15 days. This ensures that models remain active and accessible, supporting continuous AI-driven operations.
11. Optional: Continuous Fine-Tuning
Continuous fine-tuning is an advanced technique that treats your existing tuned model as a base, allowing you to run the fine-tuning process again with new training data. This iterative approach can further refine the model, incorporating new insights and user feedback to enhance its performance. Continuous fine-tuning is particularly useful for evolving applications where user interactions and data are constantly changing.
By leveraging new training data based on user prompts and responses, continuous fine-tuning ensures that the model remains relevant and effective, adapting to new requirements and challenges.
12. Cost Analysis
The next step involves uploading your carefully prepared training data to Azure AI Foundry. This can be done directly from your local files, but a more efficient method is to use Azure Storage, especially if you’ve created your training sets using services such as Microsoft Fabric. By pre-uploading the training data to Azure Blob Storage, you can streamline the process and reduce the risk of interruptions or errors during the upload.
Azure AI Foundry acts as a centralized hub, allowing you to manage and access all your training data in one location. This consolidation of resources enhances the workflow and is particularly beneficial when fine-tuning models for specific applications. Having all necessary resources in one place makes the task more straightforward and reduces the complexity often associated with managing diverse datasets.
In addition, using Azure Storage and other integrated services like Microsoft Fabric can offer further benefits such as increased storage capacity, improved data security, and easier scalability. These features are invaluable when dealing with large and complex datasets, ensuring that your training data remains accessible and secure at all times.
Overall, by leveraging Azure AI Foundry and related services, you can create a more efficient and organized workflow, allowing you to focus on the critical aspects of your AI and machine learning projects. This strategic approach not only saves time but also enhances the quality and performance of your models.